Might be mainly driven by an improved tokenizer.
I would be shocked if this is the main driver, they claim that English only has 1.1x fewer tokens, but seem to claim much bigger speed-ups
Neel Nanda
Karma: 6,984
(EDIT: I just saw Ryan posted a comment a few minutes before mine, I agree substantially with it)
As a Google DeepMind employee I’m obviously pretty biased, but this seems pretty reasonable to me, assuming it’s about alignment/similar teams at those labs? (If it’s about capabilities teams, I agree that’s bad!)
I think the alignment teams generally do good and useful work, especially those in a position to publish on it. And it seems extremely important that whoever makes AGI has a world-class alignment team! And some kinds of alignment research can only really be done with direct access to frontier models. MATS scholars tend to be pretty early in their alignment research career, and I also expect frontier lab alignment teams are a better place to learn technical skills especially engineering, and generally have a higher talent density there.
UK AISI/US AISI/METR seem like solid options for evals, but basically just work on evals, and Ryan says down thread that only 18% of scholars work on evals/demos. And I think it’s valuable both for frontier labs to have good evals teams and for there to be good external evaluators (especially in government), I can see good arguments favouring either option.
44% of scholars did interpretability, where in my opinion the Anthropic team is clearly a fantastic option, and I like to think DeepMind is also a decent option, as is OpenAI. Apollo and various academic labs are the main other places you can do mech interp. So those career preferences seem pretty reasonable to me there for interp scholars.
17% are on oversight/control, and for oversight I think you generally want a lot of compute and access to frontier models? I am less sure for control, and think Redwood is doing good work there, but as far as I’m aware they’re not hiring.
This is all assuming that scholars want to keep working in the same field they did MATS for, which in my experience is often but not always true.
I’m personally quite skeptical of inexperienced researchers trying to start new orgs—starting a new org and having it succeed is really, really hard, and much easier with more experience! So people preferring to get jobs seems great by my lights
Note that number of scholars is a much more important metric than number of mentors when it comes to evaluating MATS resources, as scholar per mentors varies a bunch (eg over winter I had 10 scholars, which is much more than most mentors). Harder to evaluate from the outside though!
Thanks, I’d be very curious to hear if this meets your bar for being impressed, or what else it would take! Further evidence:
Passing the Twitter test (for at least one user)
Being used by Simon Lerman, an author on Bad LLama (admittedly with help of Andy Arditi, our first author) to jailbreak LLaMA3 70B to help create data for some red-teaming research, (EDIT: rather than Simon choosing to fine-tune it, which he clearly knows how to do, being a Bad LLaMA author).
Nnsight, pyvene, inseq, torchlens are other libraries coming to mind that it would be good to discuss in a related work. Also penzai in JAX
I hadn’t seen the latter, thanks for sharing!
Agreed, it seems less elegant, But one guy on huggingface did a rough plot the cross correlation, and it seems to show that the directions changes with layer https://huggingface.co/posts/Undi95/318385306588047#663744f79522541bd971c919. Although perhaps we are missing something.
Idk. This shows that if you wanted to optimally get rid of refusal, you might want to do this. But, really, you want to balance between refusal and not damaging the model. Probably many layers are just kinda irrelevant for refusal. Though really this argues that we’re both wrong, and the most surgical intervention is deleting the direction from key layers only.
Thanks! I’m personally skeptical of ablating a separate direction per block, it feels less surgical than a single direction everywhere, and we show that a single direction works fine for LLAMA3 8B and 70B
The transformer lens library does not have a save feature :(
Note that you can just do torch.save(FILE_PATH, model.state_dict()) as with any PyTorch model.
Thanks for making these! How expensive is it?
Makes sense! Sounds like a fairly good fit
It just seems intuitively like a natural fit: Everyone in mech interp needs to inspect models. This tool makes it easier to inspect models.
Another way of framing it: Try to write your paper in such a way that a mech interp researcher reading it says “huh, I want to go and use this library for my research”. Eg give examples of things that were previously hard that are now easy.
Looks relevant to me on a skim! I’d probably want to see some arguments in the submission for why this is useful tooling for mech interp people specifically (though being useful to non mech interp people too is a bonus!)
That’s awesome, and insanely fast! Thanks so much, I really appreciate it
Nope to both of those, though I think both could be interesting directions!
Nah I think it’s pretty sketchy. I personally prefer mean ablation, especially for residual stream SAEs where zero ablation is super damaging. But even there I agree. Compute efficiency hit would be nice, though it’s a pain to get the scaling laws precise enough
For our paper this is irrelevant though IMO because we’re comparing gated and normal SAEs, and I think this is just scaling by a constant? It’s at least monotonic in CE loss degradation
I don’t think we really engaged with that question in this post, so the following is fairly speculative. But I think there’s some situations where this would be a superior technique, mostly low resource settings where doing a backwards pass is prohibitive for memory reasons, or with a very tight compute budget. But yeah, this isn’t a load bearing claim for me, I still count it as a partial victory to find a novel technique that’s a bit worse than fine tuning, and think this is significantly better than prior interp work. Seems reasonable to disagree though, and say you need to be better or bust
+1 to Rohin. I also think “we found a cheaper way to remove safety guardrails from a model’s weights than fine tuning” is a real result (albeit the opposite of useful), though I would want to do more actual benchmarking before we claim that it’s cheaper too confidently. I don’t think it’s a qualitative improvement over what fine tuning can do, thus hedging and saying tentative
Thanks! Broadly agreed
For example, I think our understanding of Grokking in late 2022 turned out to be importantly incomplete.
I’d be curious to hear more about what you meant by this
It was added recently and just added to a new release, so
pip install transformer_lensshould work now/soon (you want v1.16.0 I think), otherwise you can install from the Github repo
There’s been a fair amount of work on activation steering and similar techniques,, with bearing in eg sycophancy and truthfulness, where you find the vector and inject it eg Rimsky et al and Zou et al. It seems to work decently well. We found it hard to bypass refusal by steering and instead got it to work by ablation, which I haven’t seen much elsewhere, but I could easily be missing references
I see this is strongly disagree voted—I don’t mind, but I’d be curious for people to reply with which parts they disagree with! (Or at least disagree react to specific lines). I make a lot of claims in that comment, though I personally think they’re all pretty reasonable. The one about not wanting inexperienced researchers to start orgs, or “alignment teams at scaling labs are good actually” might be spiciest?